Investigation of low cycle fatigue on large gas turbine ...€¦ · Investigation of low cycle...

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Investigation of low cycle fatigue on large gas turbine casings under consideration of geometric tolerances Untersuchung der Lebensdauer von Gasturbinengehäusen unter Berücksichtigung geometrischer Streuungen siemens.com/powergeneration Uwe Lohse Siemens AG Large Gas Turbines; Burkhard Voss Siemens AG Large Gas Turbines; Thorsten Lowitz Siemens AG Large Gas Turbines Holger Schulze Spüntrup ITB Dortmund; Sebastian Wolff DYNARDO Austria Restricted © Siemens AG 2017

Transcript of Investigation of low cycle fatigue on large gas turbine ...€¦ · Investigation of low cycle...

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Investigation of low cycle fatigue on large gas turbine casings

under consideration of geometric tolerances

Untersuchung der Lebensdauer von Gasturbinengehäusen unter

Berücksichtigung geometrischer Streuungen

siemens.com/powergeneration

Uwe Lohse Siemens AG Large Gas Turbines; Burkhard Voss Siemens AG Large Gas Turbines; ThorstenLowitz Siemens AG Large Gas Turbines

Holger Schulze Spüntrup ITB Dortmund; Sebastian Wolff DYNARDO Austria

Restricted © Siemens AG 2017

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Where we are – Gas Turbine Locations & JointVenture Partners

JV location

Gas Turbines location

Muelheim, Germany

SGTT,

St. Petersburg, Russia

SEPG/SGTP,

Shanghai, China

Erlangen, Germany

Charlottesville, USA

Gurgaon, India

Shanghai, China

Jupiter, USAOrlando, USA

Charlotte Plant, USA

Berlin Plant, Germany

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The Siemens gas turbines portfolio:The right engine for every requirement

Heavy-dutygas turbines

Industrialgas turbines

Aeroderivativegas turbines

50H

z50

Hz

or60

Hz

60H

z425 MW

329 MW

187 MW

310 MW

250 MW

117 MW

53 to 66 / 54 to 62 MW

27 to 33 / 28 to 34 MW

4 to 7 MW

48 to 54 MW

40 / 41 MW

33 / 34 MW

24 / 25 MW

13 to 14 / 13 to 15 MW

8 / 8 to 9 MW

5 / 6 MW

SGT5-8000H

SGT5-4000F

SGT5-2000E

SGT6-8000H

SGT6-5000F

SGT6-2000E

SGT-A65 TR

SGT-800

SGT-A45 TR

SGT-750

SGT-700

SGT-A30 RB andSGT-A35 RB

SGT-600

SGT-400

SGT-300

SGT-100

SGT-A05 AE

39 to 44 MW

Power Generation / Mechanical Drive, Performance at ISO conditions

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SGT5-8000H during assembly at Berlin plant

Efficiency:ƒ GT > 40 %

ƒ GUD 61 %

With district heating~85 %

Power:• GT 425 MW

• CCPP 630 MW

Weight: ~445 t

Length: ~12,6 m

Diameter: ~5,5 m

Fleet: > 74 units

WOST 2017

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SGT5-8000H Plant Lausward DuesseldorfPress Release

Das neue Erdgaskraftwerk ist am Netz

Das Gas- und Dampfkraftwerk ist ein Meister derWiederverwertung von Energie. DieReibungsverluste, die bei der Umwandlung vonEnergie in Strom auftreten sind deshalb geringerals bei anderen Kraftwerken. Durch dasZusammenspiel mit Fernwärme erreicht dasKraftwerk einen Wirkungsgrad von 85 Prozent(bei Steinkohlekraftwerken liegt der Wert bei 45Prozent). Dank umweltschonender Technikwerden im ersten Betriebsjahr 600.000 TonnenCO2 eingespart, im Jahr 2025 sollen es übereine Million Tonnen sein.

Source: RP 2016-01-29

Düsseldorf. Siemens hat am 22. Januar2016 das schlüsselfertig errichtete Gas- undDampfturbinen (GuD)-Kraftwerk amStandort Lausward im Düsseldorfer Hafenan den Kunden und Betreiber StadtwerkeDüsseldorf AG übergeben. Das Kraftwerkwartet gleich mit drei neuen Rekorden imweltweiten Vergleich auf. In einer Testfahrtvor Abnahme wurde im Block „Fortuna“ einemaximale elektrische Leistung von 603,8Megawatt (MW) erreicht, das ist eine neueBestmarke für ein GuD-Kraftwerk dieserAuslegung. Gleichzeitig wurde einWirkungsgrad von rund 61,5 Prozentnachgewiesen – ein neuer Weltrekord.

Source:

WAZ

2016-01-28

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Key thermodynamic values for bolting design

-40°C +50°C

~440°C >10 bar

~580° to ~650°C

Compressor section

Combustion section

Turbine section

Exhaust section

SUCK

SQUEEZEBANG

BLOW

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Motivation

Customer requirements

• Effective• Reliable• Available• Flexible• Cost effective

Pushing design closer to the limits to fulfillthe customer requirements

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Motivation

With a classical deterministic approach it is difficult to fulfillthe customer needs.Safety factors for every impacted parameter used• Material distribution• Boundary condition• Geometry variation

Probabilistic approach helps to fulfill the customerrequirements

Only a probabilistic approach can considerthe interaction of the impacted parameters

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Effects on structural reliability / availability

This project deals only with thegeometry’s effect

MaterialMaterial scatter of properties and fatigue curveSize effect also known as weakest link theory

GeometryManufacturing tolerances

LoadingTransientEnvironmentOperation

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The idea

Quantify geometry scatter and assess the effects on thestructural behavior and design life

How to:• 3D-Scan existing geometries• Compare scanned and nominal geometry• Get the deviation of the actual geometry from the nominal geometry• Apply the deviation on a FE-Model• Compare scanned and nominal geometry and their utilization

Repeat for many different deviated geometries to obtainrepresentative data

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Challenges on scanning

• Huge amount of nodes from 180°scanNodes > 23.000.000

• Limited access for one scanner position due to the size of the object

• Limited time to scan in the production process

• Limited sample size due to long lead times

• Capability to repair and defeature scans required(actual with GEOMAGIC or GOM possible)

Improvement on scanning technic andtime wishful for daily user

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Impression of laser scan measurement

Photogrammetry system GOM Tritop incombination with the GOM ATOS Triple Scan –Blue Light Scanner.

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Challenges on structure mechanic

Actual solution: use a sub model of the90°FEA model

3D FEA including bolting contact and thermal transient required for realistic stressesfor LCF assessment

840.000 Nodes380.000 Elements

>30 GB Disc space required~ 3 Days on medium HPC hardware⇑ Project time to run ~100 samples in robust analysis not feasible.

⇑ Disadvantage! Loss of global secondary membrane and bending effect on submodel.

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Challenges on structure mechanic

Trade-off between calculation time andresult quality

240.000 Nodes

~7 GB Disc space per sample required~ 90 min on medium HPC hardware⇑ ~6 Days to run ~100 samples

840.000 Nodes

>30 GB Disc space required~ 3 Days on medium HPC hardware⇑ Project time to run ~100 samples

in robust analysis not feasible.

Submodel

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Example of the sub Modell

Not machined surface.Casting tolerance according ISO 8062

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Statistics on Structures – How it works

Statistics on Structures(SoS)

Scan Nominal geometry

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Statistics on Structures – How it works

FE-Modell

Scan1 Sample = 1 Scalar value

for the geometry deviationper node and per scan

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Example of scanning results based on 8 samples

Measured perpendicular to the surface coordinate; Normal vector direction

++

++

++

-

Mean deviation

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Example of scanning results based on 8 samples

Standard deviation

Measured perpendicular to the surface coordinate; Normal vector directionBasic statistic quantities can already beobtained

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Example of scanning results based on 8 samples

Casting tolerances according ISO 8062-3

Tolerance class n+1

Tolerance class n

Absolute deviation: Mean+3∙Sigma (~99.73%)

Basic quantities show critical locations forquality checks

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Method to work with limited sample size

Empiric random field > 100 scans

Semi synthetic field ~5 scans used to calculate meanvalue and standard deviation. Field simulated at randomwith mean an STDDEV from small number of scans

Full synthetic Random simulations based on experiencesfrom other projects or local CTQ records (critical to quality):CTQ are standard of the quality process but only local andcan‘t include the full field information.

Decreased effort tocollect data

Decreased effort toretrieve high quality

results

Compromise between effort to collect data and the resultquality needs to be discussed between the involved

parties before the project starts.

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Semi synthetic field

Mean deviation

Standard deviation

=

Spectraldecomposition

Linear combination of mean value and randomly scaled scatter shapes

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It‘s possible to create an arbitrary amountof different geometry variations

Semi synthetic field

≈ +

+ ∙ (+1,5)∙ (−1,5)

Linear combination of mean value and randomly scaled scatter shapes

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Workflow / Theory

SoS

Scan

Model

n Designpointswith n Solutions

Post-

processing

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Workflow / Theory

SoS

Post-

processing

Mean temperature Standard deviation of temperature

SoS

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Workflow / Theory

SoS

Post-

processing

SoS

=

Spectraldecomposition

Geometry deviation Temperature distribution

Scat

terS

hape

1Sc

atte

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pe2

F-CoP

Correlation of geometry scatter andtemperature scatter

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Workflow / Theory

SoS

Post-

processing

SoS

LCF-

Evaluation

Mean design life

Similarly, the workflow allows correlationof geometry scatter and design life scatter

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Design Life

Mean design life

Considering geometry scatter shows increased as wellas decreased design life⇓ Assessment of nominal

geometry is globally a good approach

> + 5%

< - 5%

Design life difference dueto probabilstic approach

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Design Life

Mean design life

However, local scatter of design life can only beconsidered by probabilistic approach and may lead to

different highly utilized locations

: =

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Design Life

Mean design life

− ∙− ∙

-3σ Design lifeMinimal values for 99.7% of real geometries

However, local scatter of design life can only beconsidered by probabilistic approach and may lead to

different highly utilized locations

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Summary

• Synthetic random fields allow probabilistic assessment of structures with few availablescans

• Software allows correlation of geometry scatter and result scatter

• Correlation of input and output allows identification of locations of uncritical geometryscatter with respect to design life. Tolerances can be adjusted accordingly.

• Easy statistical evaluation of the results plotted directly on the model in SoS

• Due to consideration of geometry scatter according safety factors may be adjusted

• Still some work to do...• Increase of sample size• Automatization of process• ...

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Contact and Acknowledgement

Uwe Lohse [email protected] Voss [email protected] Lowitz [email protected] Wolff [email protected] Schulze Spüntrup [email protected]

The presentation is based on the master thesis of Holger Schulze Spüntrup at Fachhochschule Dortmund, University ofApplied SciencesReferent Prof. Marius Geller, Fachhochschule DortmundCo-referent Sebastian Wolff, DYNARDO Austria GmbHTechnical support Dr. Frank Bremer

ITB

Pictures & logos taken from: www.dynardo.de and www.ansys.com

Ingenieurgesellschaft für technische Berechnungen mbHEuropaplatz 7, 44269 DortmundTel.: +49 (0)231 94 53 65 - 21Fax: +49 (0)231 94 53 65 - 11Geschäftsführer: Dr. Frank BrehmerHandelsregister Dortmund: HRB 16440

www.itb-fem.de

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Thanks for your attention!

WOST 2017

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Disclaimer

This document contains forward-looking statements and information – that is, statements related to future, not past,events. These statements may be identified either orally or in writing by words as “expects”, “anticipates”, “intends”,“plans”, “believes”, “seeks”, “estimates”, “will” or words of similar meaning. Such statements are based on our currentexpectations and certain assumptions, and are, therefore, subject to certain risks and uncertainties. A variety of factors,many of which are beyond Siemens’ control, affect its operations, performance, business strategy and results and couldcause the actual results, performance or achievements of Siemens worldwide to be materially different from any futureresults, performance or achievements that may be expressed or implied by such forward-looking statements. For us,particular uncertainties arise, among others, from changes in general economic and business conditions, changes incurrency exchange rates and interest rates, introduction of competing products or technologies by other companies,lack of acceptance of new products or services by customers targeted by Siemens worldwide, changes in businessstrategy and various other factors. More detailed information about certain of these factors is contained in Siemens’filings with the SEC, which are available on the Siemens website, www.siemens.com and on the SEC’s website,www.sec.gov. Should one or more of these risks or uncertainties materialize, or should underlying assumptions proveincorrect, actual results may vary materially from those described in the relevant forward-looking statement asanticipated, believed, estimated, expected, intended, planned or projected. Siemens does not intend or assume anyobligation to update or revise these forward-looking statements in light of developments which differ from thoseanticipated.

Trademarks mentioned in this document are the property of Siemens AG, it's affiliates or their respective owners.

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